计算机工程与应用 ›› 2007, Vol. 43 ›› Issue (18): 135-137.
• 网络、通信与安全 • 上一篇 下一篇
赵晓峰1,叶 震2
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ZHAO Xiao-feng1,YE Zhen2
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摘要： 传统的决策树分类方法（如ID3和C4．5）,对于相对小的数据集是很有效的。但是,当这些算法用于入侵检测这样的非常大的、现实世界中的数据时,其有效性就显得不足。采用了一种基于随机模型的决策树算法,在保证分类准确率的基础上,减少了对系统资源的占用,并通过对比实验表明该算法在对计算机入侵数据的分类上有着出色的表现。
Abstract: The traditional decision tree category methods（such as:ID3,C4.5）are effective on small data sets.But,when these methods are applied to massive data of IDS,its effectivity appears to be not enough.In this paper,a random model based decision tree algorithm is applied,and it is verified by experiment that this algorithm is evidently powerful for IDS.
赵晓峰1,叶 震2. 基于加权多随机决策树的入侵检测分类算法[J]. 计算机工程与应用, 2007, 43(18): 135-137.
ZHAO Xiao-feng1,YE Zhen2. Research on weighted multi-random decision tree and its application to intrusion detection[J]. Computer Engineering and Applications, 2007, 43(18): 135-137.
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